Steatosis In Liver Donors Research Articles

LiverColor: An Artificial Intelligence Platform for Liver Graft Assessment.

Hepatic steatosis, characterized by excess fat in the liver, is the main reason for discarding livers intended for transplantation due to its association with increased postoperative complications. The current gold standard for evaluating hepatic steatosis is liver biopsy, which, despite its accuracy, is invasive, costly, slow, and not always feasible during liver procurement. Consequently, surgeons often rely on subjective visual assessments based on the liver's colour and texture, which are prone to errors and heavily depend on the surgeon's experience. The aim of this study was to develop and validate a simple, rapid, and accurate method for detecting steatosis in donor livers to improve the decision-making process during liver procurement. We developed LiverColor, a co-designed software platform that integrates image analysis and machine learning to classify a liver graft into valid or non-valid according to its steatosis level. We utilized an in-house dataset of 192 cases to develop and validate the classification models. Colour and texture features were extracted from liver photographs, and graft classification was performed using supervised machine learning techniques (random forests and support vector machine). The performance of the algorithm was compared against biopsy results and surgeons' classifications. Usability was also assessed in simulated and real clinical settings using the Mobile Health App Usability Questionnaire. The predictive models demonstrated an area under the receiver operating characteristic curve of 0.82, with an accuracy of 85%, significantly surpassing the accuracy of visual inspections by surgeons. Experienced surgeons rated the platform positively, appreciating not only the hepatic steatosis assessment but also the dashboarding functionalities for summarising and displaying procurement-related data. The results indicate that image analysis coupled with machine learning can effectively and safely identify valid livers during procurement. LiverColor has the potential to enhance the accuracy and efficiency of liver assessments, reducing the reliance on subjective visual inspections and improving transplantation outcomes.

Artificial intelligence-aided steatosis assessment in donor livers according to the Banff consensus recommendations.

Severe macrovesicular steatosis in donor livers is associated with primary graft dysfunction. The Banff Working Group on Liver Allograft Pathology has proposed recommendations for steatosis assessment of donor liver biopsy specimens with a consensus for defining "large droplet fat" (LDF) and a 3-step algorithmic approach. We retrieved slides and initial pathology reports from potential liver donor biopsy specimens from 2010 to 2021. Following the Banff approach, we reevaluated LDF steatosis and employed a computer-assisted manual quantification protocol and artificial intelligence (AI) model for analysis. In a total of 113 slides from 88 donors, no to mild (<33%) macrovesicular steatosis was reported in 88.5% (100/113) of slides; 8.8% (10/113) was reported as at least moderate steatosis (≥33%) initially. Subsequent pathology evaluation, following the Banff recommendation, revealed that all slides had LDF below 33%, a finding confirmed through computer-assisted manual quantification and an AI model. Correlation coefficients between pathologist and computer-assisted manual quantification, between computer-assisted manual quantification and the AI model, and between the AI model and pathologist were 0.94, 0.88, and 0.81, respectively (P < .0001 for all). The 3-step approach proposed by the Banff Working Group on Liver Allograft Pathology may be followed when evaluating steatosis in donor livers. The AI model can provide a rapid and objective assessment of liver steatosis.

Effect of Donor Age and Liver Steatosis on Potential of Decellularized Liver Matrices to be used as a Platform for iPSC-Hepatocyte Culture.

Decellularization of discarded whole livers and their recellularization with patient-specific induced pluripotent stem cells (iPSCs) to develop a functional organ is a promising approach to increasing the donor pool. The effect of extracellular matrix (ECM) of marginal livers on iPSC-hepatocyte differentiation and function has not been shown. To test the effect of donor liver ECM age and steatosis, young and old, as well as no, low, and high steatosis livers, are decellularized. All livers are decellularized successfully. High steatosis livers have fat remaining on the ECM after decellularization. Old donor liver ECM induces lower marker expression in early differentiation stages, compared to young liver ECM, while this difference is closed at later stages and do not affect iPSC-hepatocyte function significantly. High steatosis levels of liver ECM lead to higher albumin mRNA expression and secretion while at later stages of differentiation expression of major cytochrome (CYP) 450enzymes is highest in low steatosis liver ECM. Both primary human hepatocytes and iPSC-hepatocytes show an increase in fat metabolism marker expression with increasing steatosis levels most likely induced by excess fat remaining on the ECM. Overall, removal of excess fat from liver ECM may be needed for inducing proper hepatic function after recellularization.

Improved assessment of donor liver steatosis using Banff consensus recommendations and deep learning algorithms

The Banff Liver Working Group recently published consensus recommendations for steatosis assessment in donor liver biopsy, but few studies reported their use and no automated deep-learning algorithms based on the proposed criteria have been developed so far. We evaluated Banff recommendations on a large monocentric series of donor liver needle biopsies by comparing pathologists' scores with those generated by convolutional neural networks (CNNs) we specifically developed for automated steatosis assessment. We retrospectively retrieved 292 allograft liver needle biopsies collected between January 2016 and January 2020 and performed steatosis assessment using a former intra-institution method (pre-Banff method) and the newly introduced Banff recommendations. Scores provided by pathologists and CNN models were then compared, and the degree of agreement was measured with the intraclass correlation coefficient (ICC). Regarding the pre-Banff method, poor agreement was observed between the pathologist and CNN models for small droplet macrovesicular steatosis (ICC: 0.38), large droplet macrovesicular steatosis (ICC: 0.08), and the final combined score (ICC: 0.16) evaluation, but none of these reached statistically significance. Interestingly, significantly improved agreement was observed using the Banff approach: ICC was 0.93 for the low-power score (p <0.001), 0.89 for the high-power score (p <0.001), and 0.93 for the final score (p <0.001). Comparing the pre-Banff method with the Banff approach on the same biopsy, pathologist and CNN model assessment showed a mean (±SD) percentage of discrepancy of 26.89 (±22.16) and 1.20 (±5.58), respectively. Our findings support the use of Banff recommendations in daily practice and highlight the need for a granular analysis of their effect on liver transplantation outcomes. We developed and validated the first automated deep-learning algorithms for standardized steatosis assessment based on the Banff Liver Working Group consensus recommendations. Our algorithm provides an unbiased automated evaluation of steatosis, which will lay the groundwork for granular analysis of steatosis's short- and long-term effects on organ viability, enabling the identification of clinically relevant steatosis cut-offs for donor organ acceptance. Implementing our algorithm in daily clinical practice will allow for a more efficient and safe allocation of donor organs, improving the post-transplant outcomes of patients.

Detection of Large-Droplet Macrovesicular Steatosis in Donor Livers Based on Segment-Anything Model

Liver transplantation is an effective treatment for end-stage liver disease, acute liver failure, and primary hepatic malignancy. However, the limited availability of donor organs remains a challenge. Severe large-droplet fat (LDF) macrovesicular steatosis, characterized by cytoplasmic replacement with large fat vacuoles, can lead to liver transplant complications. Artificial intelligence models, such as segmentation and detection models, are being developed to detect LDF hepatocytes. The Segment-Anything Model, utilizing the DEtection TRansformer architecture, has the ability to segment objects without prior knowledge of size or shape. We investigated the Segment-Anything Model's potential to detect LDF hepatocytes in liver biopsies. Pathologist-annotated specimens were used to evaluate model performance. The model showed high sensitivity but compromised specificity due to similarities with other structures. Filtering algorithms were developed to improve specificity. Integration of the Segment-Anything Model with rule-based algorithms accurately detected LDF hepatocytes. Improved diagnosis and treatment of liver diseases can be achieved through advancements in artificial intelligence algorithms for liver histology analysis.

Development of a portable device to quantify hepatic steatosis in potential donor livers.

An accurate estimation of liver fat content is necessary to predict how a donated liver will function after transplantation. Currently, a pathologist needs to be available at all hours of the day, even at remote hospitals, when an organ donor is procured. Even among expert pathologists, the estimation of liver fat content is operator-dependent. Here we describe the development of a low-cost, end-to-end artificial intelligence platform to evaluate liver fat content on a donor liver biopsy slide in real-time. The hardware includes a high-resolution camera, display, and GPU to acquire and process donor liver biopsy slides. A deep learning model was trained to label and quantify fat globules in liver tissue. The algorithm was deployed on the device to enable real-time quantification and characterization of fat content for transplant decision-making. This information is displayed on the device and can also be sent to a cloud platform for further analysis.

Simultaneous 3-way Paired Exchange Liver Transplantation Without Nondirected Donation: Novel Strategy to Expand the Donor Pool.

Simultaneous 3-way Paired Exchange Liver Transplantation Without Nondirected Donation: Novel Strategy to Expand the Donor Pool.

Gamma-glutamyltransferase predicts macrovesicular liver graft steatosis – an analysis of discarded liver allografts in Finland

Objective Liver-transplantation activity is limited by the shortage of grafts. Donor-liver macrovesicular steatosis predisposes to ischemia-reperfusion injury and is associated with reduced graft survival. The increasing prevalence of fatty-liver disease underlines the importance of identifying macrovesicular steatosis in potential donor livers. We analyzed liver grafts discarded for transplantation, and particularly the role of gamma-glutamyltransferase (GGT) in predicting graft steatosis. Methods One-hundred sixty rejected cadaveric-donor liver grafts were studied. Donor selection was based on clinical data, and macroscopic graft inspection. Discarded grafts were biopsied at procurement of non-liver organs. Results The most common reasons for discarding the graft were abnormal liver tests, ultrasound-verified steatosis and history of harmful alcohol use. GGT correlated moderately with macrovesicular steatosis (r = 0.52, p < 0.001), but poorly with microvesicular steatosis (r = 0.36, p < 0.001). Increased correlation between GGT and macrovesicular steatosis was observed among alcohol abusers (r = 0.67, p < 0.001). Area under the curve (AUC) of GGT for predicting >30% macrovesicular steatosis was 0.79 (95% CI 0.71–0.88), and for >60% steatosis, 0.79 (95% CI 0.68–0.90). The optimal GGT-cut off for detecting >30% and >60% macrovesicular steatosis were, respectively, 66 U/L (sensitivity 76% and specificity 68%) and 142 U/L (sensitivity 66% and specificity 83%). Among alcohol users, a GGT value >90 U/L showed 100% sensitivity for >60% macrovesicular steatosis. AUC for GGT in predicting fibrosis Stages 2–4 was 0.82 (95% CI 0.71–0.92, p < 0.001, optimal cut off 68, sensitivity 92%, specificity 61%). Conclusions Abnormal liver values, steatosis and harmful alcohol use were the main reasons for discarding liver-graft offers in Finland. GGT proved useful in predicting moderate and severe liver graft macrovesicular steatosis.

Artificial intelligence for prediction of donor liver allograft steatosis and early post-transplantation graft failure.

Donor livers undergo subjective pathologist review of steatosis before transplantation to mitigate the risk for early allograft dysfunction (EAD). We developed an objective, computer vision artificial intelligence (CVAI) platform to score donor liver steatosis and compared its capability for predicting EAD against pathologist steatosis scores. Two pathologists scored digitized donor liver biopsy slides from 2014 to 2019. We trained four CVAI platforms with 1:99 training:prediction split. Mean intersection-over-union (IU) characterized CVAI model accuracy. We defined EAD using liver function tests within 1 week of transplantation. We calculated separate EAD logistic regression models with CVAI and pathologist steatosis and compared the models' discrimination and internal calibration. From 90 liver biopsies, 25,494 images trained CVAI models yielding peak mean IU=0.80. CVAI steatosis scores were lower than pathologist scores (median 3% vs 20%, P<0.001). Among 41 transplanted grafts, 46% developed EAD. The median CVAI steatosis score was higher for those with EAD (2.9% vs 1.9%, P=0.02). CVAI steatosis was independently associated with EAD after adjusting for donor age, donor diabetes, and MELD score (aOR=1.34, 95%CI=1.03-1.75, P=0.03). The CVAI steatosis EAD model demonstrated slightly better calibration than pathologist steatosis, meriting further investigation into which modality most accurately and reliably predicts post-transplantation outcomes.

Use of steatotic donor livers for transplantation: Do they affect outcome?

AbstractBackgroundThe exact effect of donor steatosis on post‐transplant outcome is not well established. The aims of this study were to evaluate the effect of different grades of deceased donor liver steatosis on recipient outcome and to validate performance of the MEAF score in conjunction with graft steatosis.MethodsAdult recipients of 228 livers were studied. Livers were divided into three groups according to grade of steatosis; S0, no steatosis (n = 152); S1, minimal to mild steatosis &lt;33% (n = 62); S2, moderate to severe steatosis 33%–66% (n = 14). Donor, recipient data and outcomes of transplantation were evaluated.ResultsModerate to severe steatosis (S2) was associated with older donors and higher donor BMI. Postoperative laboratory values were significantly higher in S2. Incidence of EAD did not differ significantly with no PNFs. MEAF score correlated well with EAD. S0 had a significant shorter ICU stay. Mortality did not differ significantly. Median MEAF score was higher for S2 than S0 and S1 (5.80 versus 4.73 and 4.49).ConclusionSteatosis was associated with older, more obese donors, but did not adversely affect the postoperative outcomes. Steatotic graft recipients had more renal impairment and longer ICU stay. MEAF score might be an accurate method for assessing the early graft function in conjunction with steatosis.

The use of pre-trained deep learning models for the photographic assessment of donor livers for transplantation

Aim: Hepatic steatosis is a recognised major risk factor for primary graft failure in liver transplantation. In general, the global fat burden is measured by the surgeon using a visual assessment. However, this can be augmented by a histological assessment, although there is often inter-observer variation in this regard as well. In many situations the assessment of the liver relies heavily on the experience of the observer and more experienced surgeons will accept organs that more junior surgeons feel are unsuitable for transplantation. Often surgeons will err on the side of caution and not accept a liver for fear of exposing recipients to excessive risk of death. Methods: In this study, we present the use of deep learning for the non-invasive evaluation of donor liver organs. Transfer learning, using deep learning models such as the Visual Geometry Group (VGG) Face, VGG16, Residual Neural Network 50 (ResNet50), Dense Convolutional Network 121 (DenseNet121) and MobileNet are utilised for effective pattern extraction from partial and whole liver. Classification algorithms such as Support Vector Machines, k-Nearest Neighbour, Logistic Regression, Decision Tree and Linear Discriminant Analysis are then used for the final classification to identify between acceptable or non-acceptable donor liver organs. Results: The proposed method is distinct in that we make use of image information both from partial and whole liver. We show that common pre-trained deep learning models can be used to quantify the donor liver steatosis with an accuracy of over 92%. Conclusion: Machine learning algorithms offer the tantalising prospect of standardising the assessment and the possibility of using more donor organs for transplantation.

Non-invasive real-time assessment of hepatic macrovesicular steatosis in liver donors: Hypothesis, design and proof-of-concept study.

Macrovesicular Steatosis (MS) is an independent risk factor for adverse post-liver transplant (LT) outcomes. The degree of MS is intimately related to the viability of the liver graft, which in turn is crucial to the success of the operation. An ideal liver graft should have no MS and most centres would find it unacceptable to use a donor liver with severe MS for LT. While a formal liver biopsy is the gold-standard diagnostic test for MS, given the logistical and time constraints it is not universally feasible. Other tests like a frozen section biopsy are plagued by issues of fallibility with reporting and sampling bias making them inferior to a liver biopsy. Hence, the development of an accurate, non-invasive, easy-to-use, handheld, real-time device for quantification of MS would fill this lacuna in the deceased donor selection process. We present the hypothesis, design and proof-of-concept of a study, which aims to standardise and determine the feasibility and accuracy of a novel handheld device applying the principle of diffuse reflectance spectroscopy for real-time quantification of MS.

Inhibition of MLKL Attenuates Necroptotic Cell Death in a Murine Cell Model of Ischaemia Injury.

Background: Steatosis in donor livers poses a major risk of organ dysfunction due to their susceptibility to ischaemia-reperfusion (I/R) injury during transplant. Necroptosis, a novel form of programmed cell death, is orchestrated by receptor-interacting protein kinase 1 (RIPK1), receptor-interacting protein kinase 3 (RIPK3) and mixed-lineage kinase domain-like pseudokinase (MLKL), has been implicated in I/R injury. Here we investigated the mechanisms of cell death pathways in an in vitro model of hepato-steatotic ischaemia. Methods: Free fatty acid (FFA) treated alpha mouse liver 12 (AML-12) cells were incubated in oxygen-glucose-deprivation (OGD) conditions as seen during ischaemia. Results: We found that OGD triggered upregulation of insoluble fraction of RIPK3 and MLKL in FFA + OGD cells compared to FFA control cells. We report that intervention with small interfering (si) MLKL and siRIPK3 significantly attenuated cell death in FFA + OGD cells. Absence of activated CASPASE8 and cleaved-CASPASE3, no change in the expression of CASPASE1 and prostaglandin-endoperoxide synthase 2 (Ptgs2) in FFA + OGD treated cells compared to FFA control cells indicated that apoptosis, pyroptosis and ferroptosis, respectively, are unlikely to be active in this model. Conclusion: Our findings indicate that RIPK3-MLKL dependent necroptosis contributed to cell death in our in vitro model. Both MLKL and RIPK3 are promising therapeutic targets to inhibit necroptosis during ischaemic injury in fatty liver.

Metabolic associated fatty liver disease: Addressing a new era in liver transplantation.

Metabolic associated fatty liver disease (MAFLD), previously termed non-alcoholic fatty liver disease, is the leading global cause of liver disease and is fast becoming the most common indication for liver transplantation. The recent change in nomenclature to MAFLD refocuses the conceptualisation of this disease entity to its metabolic underpinnings and may help to spur a paradigm shift in the approach to its management, including in the setting of liver transplantation. Patients with MAFLD present significant challenges in the pre-, peri- and post-transplant settings, largely due to the presence of medical comorbidities that include obesity, metabolic syndrome and cardiovascular risk factors. As the community prevalence of MAFLD increases concurrently with the obesity epidemic, donor liver steatosis is also a current and future concern. This review outlines current epidemiology, nomenclature, management issues and outcomes of liver transplantation in patients with MAFLD.

Outcome for Pediatric Recipients of Macrosteatotic Liver Grafts From Living Donors.

In deceased donor liver transplantation, a donor liver with moderate (>30%) macrosteatosis used to be considered inappropriate for grafting. We examined the outcomes of children who underwent living donor liver transplantation (LDLT) at the National Center for Child Health and Development whose donor livers had moderate-to-severe macrosteatosis. Twelve children were enrolled who had received a moderate-to-severe macrosteatotic liver graft and underwent liver biopsy soon after LDLT. The primary diseases were biliary atresia in 7 patients, acute liver failure in 3 patients, glycogen storage disease type 1 in 1 patient, and primary sclerosing cholangitis in 1 patient. Median age was 11 months. There were 4 recipients who received grafts from their fathers, and 8 received grafts from their mothers. Median donor age was 35.5 years. We compared the degree of donor liver steatosis with the results of graft liver biopsies that were collected 4-105 days after LDLT. The levels of donor liver macrovesicular steatosis were moderate (34%-66%) in 9 patients and severe (>66%) in 3 patients. The nonalcoholic fatty liver disease activity score was 3 in 7 patients and 4 in 5 patients. Shortly after LDLT, 11 of 12 patients showed improvement in steatosis compared with the donor livers. One biopsy specimen taken 22 days after LDLT showed 60% macrosteatosis, which was the same as that in the donor liver. However, this patient was alive and well 6 years after LDLT. One patient died after LDLT because of infection and respiratory failure. The levels of steatosis of the donor liver grafts improved soon after LDLT in children, and the outcomes of children receiving a moderate-to-severe macrosteatotic liver from their parents were excellent.

The Utility of Noninvasive Scoring Systems for Prediction of Hepatic Steatosis in Liver Transplantation Donor Candidates

BackgroundThe degree of liver steatosis is an important factor for donor selection in living donor liver transplantation. Multidetector computed tomography (MDCT) has long been used in many transplantation centers to determine donor liver steatosis. Noninvasive scoring methods based on laboratory tests have been investigated as potential methods for altering liver biopsy and imaging techniques in evaluating the liver steatosis. In this study, we assess the utility of several noninvasive methods for the evaluation of donor hepatosteatosis. In comparison, MDCT was used for the evaluation of liver steatosis. MethodsA total of 205 donor candidates with significant hepatosteatosis were included in the study and divided into 4 groups according to the degrees of steatosis as measured by MDCT (mild, mid- to moderate, moderate to severe, and severe). In comparison, the aspartate aminotransferase–platelet ratio index (APRI), nonalcoholic fatty liver disease fibrosis score, BARD score, and FIB–4 scores were calculated. ResultsThe diagnostic performance of APRI in prediction of all degrees of hepatosteatosis on MDCT was significantly higher (P < .01). The BARD score showed the second best performance (P = .018), whereas FIB–4 and nonalcoholic fatty liver disease fibrosis score were not correlated with degree of liver steatosis on MDCT. ConclusionSome noninvasive scoring methods including APRI and BARD score seem to be more beneficial for the detection of hepatic steatosis in donor candidates and may reduce the need for other invasive and expensive diagnostic techniques.

Effect of Donor Hepatic Steatosis on Ischemia Reperfusion Injury in Liver Transplant Recipient

Ischemia reperfusion injury (IRI) is an important complication of liver transplant (LT). The donor risk index, which does not incorporate steatosis, includes several variables known to impact on allograft survival. The purpose of this study was to report on donor liver allograft steatosis and its association with severity of IRI. The aim of this study was to determine the effect of type and grade of donor liver steatosis on the occurrence and severity of IRI in LT recipients. This was an observational study conducted at a single center over a period of 37 months from July 2013 to August 2016. Liver biopsy was performed twice, initially at the time of procurement before graft perfusion for steatosis assessment. Steatosis was classified as microsteatosis (MiS) or macrosteatosis (MaS) with mild, moderate, or severe grade. Second biopsy for IRI assessment was taken before skin closure in death donor LT (DDLT) and at the time of transaminitis in postoperative period (<72hrs) in living donor LT (LDLT). IRI was graded as per neutrophil infiltrate, apoptosis, and hepatocyte cell dropout. Prevalence of IRI and association steatosis was studied along with other factors. Among 53 subjects, 35 were DDLTs and 18 were LDLTs. All live donor grafts were restricted to <15% MaS and the deceased liver grafts had different type and degree of steatosis. In DDLTs, the association between occurrence of IRI and MaS was not statistically significant (P= 0.201). In DDLTs, the mild steatosis was not significantly associated with IRI. Death donor and ischemic time were significantly associated with IRI. Child's stage and MELD scores, gender, and age were not associated with risk of IRI. Severity of IRI is significantly associated with 3-month mortality (P= 0.001). In patients with mild steatosis, IRI does not correlate with steatosis. However, more patients with moderate and severe steatosis are needed to define the relationship of the two in this group of patients.

Donor Small-Droplet Macrovesicular Steatosis Affects Liver Transplant Outcome in HCV-Negative Recipients.

Background No data are available on liver transplantation (LT) outcome and donor liver steatosis, classified as large droplet macrovesicular (Ld-MaS), small-droplet macrovesicular (Sd-MaS), and true microvesicular (MiS), taking into account the recipient Hepatitis C virus (HCV) status. Aim We investigate the impact of allograft steatosis reclassified according to the Brunt classification on early graft function and survival after LT. Methods We retrospectively reviewed 204 consecutive preischemia biopsies of grafts transplanted in our center during the period 2001-2011 according to recipient HCV status. Results The median follow-up after LT was 7.5 years (range: 0.0-16.7). In negative recipients (n=122), graft loss was independently associated with graft Sd-MaS, in multivariable Cox regression models comprehending only pre-/intraoperative variables (HR=1.03, 95%CI=1.01-1.05; P=0.003) and when including indexes of early postoperative graft function (HR=1.04, 95%CI=1.02-1.06; P=0.001). Graft Sd-MaS>15% showed a risk for graft loss > 2.5-folds in both the models. Graft Sd-MaS>15% was associated with reduced graft ATP content and, only in HCV- recipients, with higher early post-LT serum AST peaks. Conclusions In HCV-negative recipients, allografts with >15% Sd-MaS have significantly reduced graft survival and show low ATP and higher AST peaks in the immediate posttransplant period. Donors with >15% Sd-MaS have significantly higher BMI, longer ICU stays, and lower PaO2.

The “Skinny” on Assessment and Utilization of Steatotic Liver Grafts: A Systematic Review

The frequency at which steatotic deceased donor liver grafts are encountered will likely continue to increase. Utilization of liver grafts with moderate-to-severe steatosis for liver transplantation (LT) has been previously shown to be associated with increased rates of primary nonfunction and decreased recipient survival. In order to better inform clinical decision making and guide future research, critical evaluation of the literature on donor liver steatosis and posttransplantation outcome is needed. This literature review aims to provide the "skinny" on using deceased donor steatotic livers for LT.

Surgical Categorization Improve Outcomes and Organ Utilization in Liver Transplantation

Background Liver transplant (LT) varies greatly in technical complexity due to factors such as prior surgery, obesity, and portal vein thrombosis (PVT). In addition, our program often evaluates ‘orphan livers’ that require a quick, effective and accurate recipient selection. This must be taken into account when considering organ offers. In 2015, we began using an A/B/C stratification of predicted surgical complexity based on surgical history and cross-sectional imaging, as reviewed by faculty surgeons. The scores are assigned at waitlist addition and are integral to on-call workflow at the time of the liver offer. Methods Four-hundred consecutives LT recipients (Jan-15 to Dec-16) were prospectively categorize at the time of listing according with their surgical complexity in Group A: Low (n=268) LT alone, absence-moderate obesity; patent PV, no upper abdomen surgeries (except lap chole); Group B: Moderate (n=91) combined LKT, moderate–severe obesity, upper abdomen operations, PV thrombosis; and Group C: High (n=41) retransplantation, previous HB surgery or gastric bypass, PV cavernous transformation. Analysis were performed using Student t-test and Fisher exact test. Survival curves were obtained by Kaplan Meier method and log rank test. Results are in Mean + SD and Mead IQ 1-3. Results Recipients in group A had lower MELD (19.5+8.3 vs. 20.4+9.3 vs. 24.8+9.6 P=0.001) while recipients in group B had higher BMI (27.2+5.4 vs. 33.1+8 vs. 26.9+5.1 p=0.002) and high proportion of HCC (32% vs. 46% vs. 17% p=0.02). Group C had the higher incidence of previous LT (2.4% vs. 1.1% vs. 26.8% p<0.001), PV thrombosis (7.8% vs. 12% vs. 29% p=0.002) and its operation time was longer (5.3+1.6 vs. 5.5+1.3 vs. 6.4+2.1 p<0.001), ICU stay (median, IQ 2, 2-4 vs. 2.8, 2-4 vs. 3, 2-5 days p=0.001) and hospital stay (Median IQ 6, 6-14 vs. 11, 7-18 vs. 12, 8-28 days, p=0.001). All three groups had similar cold ischemia time (33.2+96 vs. 317+84 vs. 312.5+89.9 min, p=0.8) but compared with group B, group A received a higher proportion of livers from out our donor service area (57.8% vs. 43.9% vs. 58.9% p=0.001). In addition, group A received more DCD livers (12.7% vs. 5.5% vs. 0% p=0.02) and higher donor risk index livers (1.6+0.4 vs. 1.4+0.4 vs. 1.3+0.2 p<0.001). Donor liver steatosis, incidence of early allograft dysfunction and, biliary and vascular complications were similar in all 3 groups. One-year graft and patient survival were 91.8% vs. 87.9% vs. 78.1% (p=0.006) and 93.3% vs. 91.2% vs. 78.1% (p=0.001) for groups A, B, C respectively. Conclusion Pre LT categorization by surgical complexity at the time of listing correctly stratified patients’ surgical risk. Surgical complexity ranking is a tool that allows better recipient selection, recipient-donor matching and resource utilization that result in increase acceptance of liver offers.